March Madness Descriptives

R Packages

packages <- c("tidyverse", "kableExtra", "broom", "broomExtra", "gganimate")
xfun::pkg_attach(packages, message = F)

Functions

nice_tables <- function(table) {
  table %>%
    kable() %>% 
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "100%", height = "200px")
}

Let’s first look at the regular season dataset.

Regular Season

df_regular_season <- read_csv("data/RegularSeasonDetailedResults.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   WLoc = col_character()
## )

## See spec(...) for full column specifications.

glimpse(df_regular_season)

## Observations: 82,041
## Variables: 34
## $ Season  <dbl> 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 20…
## $ DayNum  <dbl> 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, …
## $ WTeamID <dbl> 1104, 1272, 1266, 1296, 1400, 1458, 1161, 1186, 1194, 14…
## $ WScore  <dbl> 68, 70, 73, 56, 77, 81, 80, 75, 71, 84, 106, 74, 66, 76,…
## $ LTeamID <dbl> 1328, 1393, 1437, 1457, 1208, 1186, 1236, 1457, 1156, 12…
## $ LScore  <dbl> 62, 63, 61, 50, 71, 55, 62, 61, 66, 56, 50, 73, 65, 48, …
## $ WLoc    <chr> "N", "N", "N", "N", "N", "H", "H", "N", "N", "H", "H", "…
## $ NumOT   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
## $ WFGM    <dbl> 27, 26, 24, 18, 30, 26, 23, 28, 28, 32, 41, 29, 26, 25, …
## $ WFGA    <dbl> 58, 62, 58, 38, 61, 57, 55, 62, 58, 67, 69, 51, 66, 56, …
## $ WFGM3   <dbl> 3, 8, 8, 3, 6, 6, 2, 4, 5, 5, 15, 7, 5, 10, 11, 10, 5, 8…
## $ WFGA3   <dbl> 14, 20, 18, 9, 14, 12, 8, 14, 11, 17, 25, 13, 19, 23, 31…
## $ WFTM    <dbl> 11, 10, 17, 17, 11, 23, 32, 15, 10, 15, 9, 9, 9, 16, 12,…
## $ WFTA    <dbl> 18, 19, 29, 31, 13, 27, 39, 21, 18, 19, 13, 11, 13, 23, …
## $ WOR     <dbl> 14, 15, 17, 6, 17, 12, 13, 13, 9, 14, 15, 6, 21, 8, 15, …
## $ WDR     <dbl> 24, 28, 26, 19, 22, 24, 18, 35, 22, 22, 29, 21, 23, 35, …
## $ WAst    <dbl> 13, 16, 15, 11, 12, 12, 14, 19, 9, 11, 21, 18, 15, 18, 2…
## $ WTO     <dbl> 23, 13, 10, 12, 14, 9, 17, 19, 17, 6, 11, 15, 17, 13, 16…
## $ WStl    <dbl> 7, 4, 5, 14, 4, 9, 11, 7, 9, 12, 10, 7, 12, 14, 18, 2, 5…
## $ WBlk    <dbl> 1, 4, 2, 2, 4, 3, 1, 2, 2, 0, 6, 1, 3, 19, 5, 6, 3, 5, 1…
## $ WPF     <dbl> 22, 18, 25, 18, 20, 18, 25, 21, 23, 13, 16, 5, 17, 13, 2…
## $ LFGM    <dbl> 22, 24, 22, 18, 24, 20, 19, 20, 24, 23, 17, 29, 24, 18, …
## $ LFGA    <dbl> 53, 67, 73, 49, 62, 46, 41, 59, 52, 52, 52, 63, 56, 64, …
## $ LFGM3   <dbl> 2, 6, 3, 6, 6, 3, 4, 4, 6, 3, 4, 10, 6, 8, 4, 7, 2, 2, 3…
## $ LFGA3   <dbl> 10, 24, 26, 22, 16, 11, 15, 17, 18, 14, 11, 22, 19, 24, …
## $ LFTM    <dbl> 16, 9, 14, 8, 17, 12, 20, 17, 12, 7, 12, 5, 11, 4, 17, 1…
## $ LFTA    <dbl> 22, 20, 23, 15, 27, 17, 28, 23, 27, 12, 17, 5, 17, 8, 27…
## $ LOR     <dbl> 10, 20, 31, 17, 21, 6, 9, 8, 13, 9, 8, 13, 14, 14, 20, 6…
## $ LDR     <dbl> 22, 25, 22, 20, 15, 22, 21, 25, 26, 23, 15, 16, 21, 26, …
## $ LAst    <dbl> 8, 7, 9, 9, 12, 8, 11, 10, 13, 10, 8, 15, 17, 12, 17, 21…
## $ LTO     <dbl> 18, 12, 12, 19, 10, 19, 30, 15, 25, 18, 17, 12, 18, 17, …
## $ LStl    <dbl> 9, 8, 2, 4, 7, 4, 10, 14, 8, 1, 7, 6, 8, 10, 7, 5, 15, 8…
## $ LBlk    <dbl> 2, 6, 5, 3, 1, 3, 4, 8, 2, 3, 3, 2, 4, 0, 7, 1, 3, 4, 5,…
## $ LPF     <dbl> 20, 16, 23, 23, 14, 25, 28, 18, 18, 18, 15, 12, 13, 17, …

We will also need to load the Teams.csv dataset to append actual team names rather than IDs.

df_teams <- read_csv("data/Teams.csv")

## Parsed with column specification:
## cols(
##   TeamID = col_double(),
##   TeamName = col_character(),
##   FirstD1Season = col_double(),
##   LastD1Season = col_double()
## )

glimpse(df_teams)

## Observations: 366
## Variables: 4
## $ TeamID        <dbl> 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 11…
## $ TeamName      <chr> "Abilene Chr", "Air Force", "Akron", "Alabama", "A…
## $ FirstD1Season <dbl> 2014, 1985, 1985, 1985, 2000, 1985, 2000, 1985, 19…
## $ LastD1Season  <dbl> 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 19…

# select only the TeamID and TeamName vars
df_teams <- df_teams %>% select(TeamID, TeamName) 

# create df for win and loss TeamID and TeamNames to append to regular season df
df_wteams <- df_teams                         
colnames(df_wteams) <- c("WTeamID", "WTeamName")
df_lteams <- df_teams                        
colnames(df_lteams) <- c("LTeamID", "LTeamName")

# add actual names using inner_join
# remove TeamID
df_regular_season <- df_regular_season %>%
  inner_join(., df_wteams, by = "WTeamID") %>%
  inner_join(., df_lteams, by = "LTeamID") %>%
  select(-contains("TeamID"))

glimpse(df_regular_season)

## Observations: 82,041
## Variables: 34
## $ Season    <dbl> 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, …
## $ DayNum    <dbl> 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13…
## $ WScore    <dbl> 68, 70, 73, 56, 77, 81, 80, 75, 71, 84, 106, 74, 66, 7…
## $ LScore    <dbl> 62, 63, 61, 50, 71, 55, 62, 61, 66, 56, 50, 73, 65, 48…
## $ WLoc      <chr> "N", "N", "N", "N", "N", "H", "H", "N", "N", "H", "H",…
## $ NumOT     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
## $ WFGM      <dbl> 27, 26, 24, 18, 30, 26, 23, 28, 28, 32, 41, 29, 26, 25…
## $ WFGA      <dbl> 58, 62, 58, 38, 61, 57, 55, 62, 58, 67, 69, 51, 66, 56…
## $ WFGM3     <dbl> 3, 8, 8, 3, 6, 6, 2, 4, 5, 5, 15, 7, 5, 10, 11, 10, 5,…
## $ WFGA3     <dbl> 14, 20, 18, 9, 14, 12, 8, 14, 11, 17, 25, 13, 19, 23, …
## $ WFTM      <dbl> 11, 10, 17, 17, 11, 23, 32, 15, 10, 15, 9, 9, 9, 16, 1…
## $ WFTA      <dbl> 18, 19, 29, 31, 13, 27, 39, 21, 18, 19, 13, 11, 13, 23…
## $ WOR       <dbl> 14, 15, 17, 6, 17, 12, 13, 13, 9, 14, 15, 6, 21, 8, 15…
## $ WDR       <dbl> 24, 28, 26, 19, 22, 24, 18, 35, 22, 22, 29, 21, 23, 35…
## $ WAst      <dbl> 13, 16, 15, 11, 12, 12, 14, 19, 9, 11, 21, 18, 15, 18,…
## $ WTO       <dbl> 23, 13, 10, 12, 14, 9, 17, 19, 17, 6, 11, 15, 17, 13, …
## $ WStl      <dbl> 7, 4, 5, 14, 4, 9, 11, 7, 9, 12, 10, 7, 12, 14, 18, 2,…
## $ WBlk      <dbl> 1, 4, 2, 2, 4, 3, 1, 2, 2, 0, 6, 1, 3, 19, 5, 6, 3, 5,…
## $ WPF       <dbl> 22, 18, 25, 18, 20, 18, 25, 21, 23, 13, 16, 5, 17, 13,…
## $ LFGM      <dbl> 22, 24, 22, 18, 24, 20, 19, 20, 24, 23, 17, 29, 24, 18…
## $ LFGA      <dbl> 53, 67, 73, 49, 62, 46, 41, 59, 52, 52, 52, 63, 56, 64…
## $ LFGM3     <dbl> 2, 6, 3, 6, 6, 3, 4, 4, 6, 3, 4, 10, 6, 8, 4, 7, 2, 2,…
## $ LFGA3     <dbl> 10, 24, 26, 22, 16, 11, 15, 17, 18, 14, 11, 22, 19, 24…
## $ LFTM      <dbl> 16, 9, 14, 8, 17, 12, 20, 17, 12, 7, 12, 5, 11, 4, 17,…
## $ LFTA      <dbl> 22, 20, 23, 15, 27, 17, 28, 23, 27, 12, 17, 5, 17, 8, …
## $ LOR       <dbl> 10, 20, 31, 17, 21, 6, 9, 8, 13, 9, 8, 13, 14, 14, 20,…
## $ LDR       <dbl> 22, 25, 22, 20, 15, 22, 21, 25, 26, 23, 15, 16, 21, 26…
## $ LAst      <dbl> 8, 7, 9, 9, 12, 8, 11, 10, 13, 10, 8, 15, 17, 12, 17, …
## $ LTO       <dbl> 18, 12, 12, 19, 10, 19, 30, 15, 25, 18, 17, 12, 18, 17…
## $ LStl      <dbl> 9, 8, 2, 4, 7, 4, 10, 14, 8, 1, 7, 6, 8, 10, 7, 5, 15,…
## $ LBlk      <dbl> 2, 6, 5, 3, 1, 3, 4, 8, 2, 3, 3, 2, 4, 0, 7, 1, 3, 4, …
## $ LPF       <dbl> 20, 16, 23, 23, 14, 25, 28, 18, 18, 18, 15, 12, 13, 17…
## $ WTeamName <chr> "Alabama", "Memphis", "Marquette", "N Illinois", "Texa…
## $ LTeamName <chr> "Oklahoma", "Syracuse", "Villanova", "Winthrop", "Geor…

Let’s break apart the tables into wins and losses so that we can manipulate the same variables at the same time.

df_regular_season_win <- df_regular_season %>%
  select(Season, DayNum, contains("W"))
colnames(df_regular_season_win) <- gsub("W", "", colnames(df_regular_season_win))

df_regular_season_loss <- df_regular_season %>%
  select(Season, DayNum, contains("L"))
colnames(df_regular_season_loss) <- gsub("L", "", colnames(df_regular_season_loss))

# data manipulation ----
# create new variables that are also easy to read
data_manipulation_reg_season <- function(data) {
  data %>%
  select(season = Season,
         n_day = DayNum,
         team_name = TeamName,
         score = Score,
         points_made = FGM,
         points_attempted = FGA,
         points_3_made = FGM3,
         points_3_attempted = FGA3,
         points_free_throws_made = FTM,
         points_free_throws_attempted = FTA,
         rebound_offensive = OR,
         rebound_defensive = DR,
         assist = Ast,
         turnovers = TO,
         steals = Stl,
         blocks = Blk,
         personal_fouls = PF) %>%
  mutate(index = 1:nrow(data),
         points_2_made = points_made - points_3_made,
         points_2_attempted = points_attempted - points_3_attempted,
         points_missed = points_attempted - points_made,
         points_2_missed = points_2_attempted - points_2_made,
         points_3_missed = points_3_attempted - points_3_made,
         points_free_throws_missed = points_free_throws_attempted - points_free_throws_made,
         points_made_percentage = points_made / points_attempted * 100,
         points_2_made_percentage = points_2_made / points_2_attempted * 100,
         points_3_made_percentage = points_3_made / points_3_attempted * 100,
         points_free_throws_made_percentage = points_free_throws_made / points_free_throws_attempted * 100)
}

df_regular_season_win <- data_manipulation_reg_season(df_regular_season_win) %>% mutate(outcome = "win")
glimpse(df_regular_season_win)

## Observations: 82,041
## Variables: 29
## $ season                             <dbl> 2003, 2003, 2003, 2003, 2003,…
## $ n_day                              <dbl> 10, 10, 11, 11, 11, 11, 12, 1…
## $ team_name                          <chr> "Alabama", "Memphis", "Marque…
## $ score                              <dbl> 68, 70, 73, 56, 77, 81, 80, 7…
## $ points_made                        <dbl> 27, 26, 24, 18, 30, 26, 23, 2…
## $ points_attempted                   <dbl> 58, 62, 58, 38, 61, 57, 55, 6…
## $ points_3_made                      <dbl> 3, 8, 8, 3, 6, 6, 2, 4, 5, 5,…
## $ points_3_attempted                 <dbl> 14, 20, 18, 9, 14, 12, 8, 14,…
## $ points_free_throws_made            <dbl> 11, 10, 17, 17, 11, 23, 32, 1…
## $ points_free_throws_attempted       <dbl> 18, 19, 29, 31, 13, 27, 39, 2…
## $ rebound_offensive                  <dbl> 14, 15, 17, 6, 17, 12, 13, 13…
## $ rebound_defensive                  <dbl> 24, 28, 26, 19, 22, 24, 18, 3…
## $ assist                             <dbl> 13, 16, 15, 11, 12, 12, 14, 1…
## $ turnovers                          <dbl> 23, 13, 10, 12, 14, 9, 17, 19…
## $ steals                             <dbl> 7, 4, 5, 14, 4, 9, 11, 7, 9, …
## $ blocks                             <dbl> 1, 4, 2, 2, 4, 3, 1, 2, 2, 0,…
## $ personal_fouls                     <dbl> 22, 18, 25, 18, 20, 18, 25, 2…
## $ index                              <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10…
## $ points_2_made                      <dbl> 24, 18, 16, 15, 24, 20, 21, 2…
## $ points_2_attempted                 <dbl> 44, 42, 40, 29, 47, 45, 47, 4…
## $ points_missed                      <dbl> 31, 36, 34, 20, 31, 31, 32, 3…
## $ points_2_missed                    <dbl> 20, 24, 24, 14, 23, 25, 26, 2…
## $ points_3_missed                    <dbl> 11, 12, 10, 6, 8, 6, 6, 10, 6…
## $ points_free_throws_missed          <dbl> 7, 9, 12, 14, 2, 4, 7, 6, 8, …
## $ points_made_percentage             <dbl> 46.55172, 41.93548, 41.37931,…
## $ points_2_made_percentage           <dbl> 54.54545, 42.85714, 40.00000,…
## $ points_3_made_percentage           <dbl> 21.42857, 40.00000, 44.44444,…
## $ points_free_throws_made_percentage <dbl> 61.11111, 52.63158, 58.62069,…
## $ outcome                            <chr> "win", "win", "win", "win", "…

df_regular_season_loss <- data_manipulation_reg_season(df_regular_season_loss) %>% mutate(outcome = "loss")
glimpse(df_regular_season_loss)

## Observations: 82,041
## Variables: 29
## $ season                             <dbl> 2003, 2003, 2003, 2003, 2003,…
## $ n_day                              <dbl> 10, 10, 11, 11, 11, 11, 12, 1…
## $ team_name                          <chr> "Oklahoma", "Syracuse", "Vill…
## $ score                              <dbl> 62, 63, 61, 50, 71, 55, 62, 6…
## $ points_made                        <dbl> 22, 24, 22, 18, 24, 20, 19, 2…
## $ points_attempted                   <dbl> 53, 67, 73, 49, 62, 46, 41, 5…
## $ points_3_made                      <dbl> 2, 6, 3, 6, 6, 3, 4, 4, 6, 3,…
## $ points_3_attempted                 <dbl> 10, 24, 26, 22, 16, 11, 15, 1…
## $ points_free_throws_made            <dbl> 16, 9, 14, 8, 17, 12, 20, 17,…
## $ points_free_throws_attempted       <dbl> 22, 20, 23, 15, 27, 17, 28, 2…
## $ rebound_offensive                  <dbl> 10, 20, 31, 17, 21, 6, 9, 8, …
## $ rebound_defensive                  <dbl> 22, 25, 22, 20, 15, 22, 21, 2…
## $ assist                             <dbl> 8, 7, 9, 9, 12, 8, 11, 10, 13…
## $ turnovers                          <dbl> 18, 12, 12, 19, 10, 19, 30, 1…
## $ steals                             <dbl> 9, 8, 2, 4, 7, 4, 10, 14, 8, …
## $ blocks                             <dbl> 2, 6, 5, 3, 1, 3, 4, 8, 2, 3,…
## $ personal_fouls                     <dbl> 20, 16, 23, 23, 14, 25, 28, 1…
## $ index                              <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10…
## $ points_2_made                      <dbl> 20, 18, 19, 12, 18, 17, 15, 1…
## $ points_2_attempted                 <dbl> 43, 43, 47, 27, 46, 35, 26, 4…
## $ points_missed                      <dbl> 31, 43, 51, 31, 38, 26, 22, 3…
## $ points_2_missed                    <dbl> 23, 25, 28, 15, 28, 18, 11, 2…
## $ points_3_missed                    <dbl> 8, 18, 23, 16, 10, 8, 11, 13,…
## $ points_free_throws_missed          <dbl> 6, 11, 9, 7, 10, 5, 8, 6, 15,…
## $ points_made_percentage             <dbl> 41.50943, 35.82090, 30.13699,…
## $ points_2_made_percentage           <dbl> 46.51163, 41.86047, 40.42553,…
## $ points_3_made_percentage           <dbl> 20.00000, 25.00000, 11.53846,…
## $ points_free_throws_made_percentage <dbl> 72.72727, 45.00000, 60.86957,…
## $ outcome                            <chr> "loss", "loss", "loss", "loss…

df_regular_season_all <- bind_rows(df_regular_season_win, df_regular_season_loss) %>% mutate(outcome = as_factor(outcome))
glimpse(df_regular_season_all)

## Observations: 164,082
## Variables: 29
## $ season                             <dbl> 2003, 2003, 2003, 2003, 2003,…
## $ n_day                              <dbl> 10, 10, 11, 11, 11, 11, 12, 1…
## $ team_name                          <chr> "Alabama", "Memphis", "Marque…
## $ score                              <dbl> 68, 70, 73, 56, 77, 81, 80, 7…
## $ points_made                        <dbl> 27, 26, 24, 18, 30, 26, 23, 2…
## $ points_attempted                   <dbl> 58, 62, 58, 38, 61, 57, 55, 6…
## $ points_3_made                      <dbl> 3, 8, 8, 3, 6, 6, 2, 4, 5, 5,…
## $ points_3_attempted                 <dbl> 14, 20, 18, 9, 14, 12, 8, 14,…
## $ points_free_throws_made            <dbl> 11, 10, 17, 17, 11, 23, 32, 1…
## $ points_free_throws_attempted       <dbl> 18, 19, 29, 31, 13, 27, 39, 2…
## $ rebound_offensive                  <dbl> 14, 15, 17, 6, 17, 12, 13, 13…
## $ rebound_defensive                  <dbl> 24, 28, 26, 19, 22, 24, 18, 3…
## $ assist                             <dbl> 13, 16, 15, 11, 12, 12, 14, 1…
## $ turnovers                          <dbl> 23, 13, 10, 12, 14, 9, 17, 19…
## $ steals                             <dbl> 7, 4, 5, 14, 4, 9, 11, 7, 9, …
## $ blocks                             <dbl> 1, 4, 2, 2, 4, 3, 1, 2, 2, 0,…
## $ personal_fouls                     <dbl> 22, 18, 25, 18, 20, 18, 25, 2…
## $ index                              <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10…
## $ points_2_made                      <dbl> 24, 18, 16, 15, 24, 20, 21, 2…
## $ points_2_attempted                 <dbl> 44, 42, 40, 29, 47, 45, 47, 4…
## $ points_missed                      <dbl> 31, 36, 34, 20, 31, 31, 32, 3…
## $ points_2_missed                    <dbl> 20, 24, 24, 14, 23, 25, 26, 2…
## $ points_3_missed                    <dbl> 11, 12, 10, 6, 8, 6, 6, 10, 6…
## $ points_free_throws_missed          <dbl> 7, 9, 12, 14, 2, 4, 7, 6, 8, …
## $ points_made_percentage             <dbl> 46.55172, 41.93548, 41.37931,…
## $ points_2_made_percentage           <dbl> 54.54545, 42.85714, 40.00000,…
## $ points_3_made_percentage           <dbl> 21.42857, 40.00000, 44.44444,…
## $ points_free_throws_made_percentage <dbl> 61.11111, 52.63158, 58.62069,…
## $ outcome                            <fct> win, win, win, win, win, win,…

Now let’s compare some winning and losing stats.

Across All Seasons

Most Wins

df_regular_season_all %>%
  group_by(team_name, outcome) %>%
  count() %>%
  ungroup() %>%
  filter(outcome == "win") %>%
  top_n(10) %>%
  arrange(desc(n)) %>%
  mutate(team_name = factor(team_name, levels = team_name)) %>%
  ggplot(., aes(x = team_name, y = n, fill = n)) + 
    geom_col() +
    scale_fill_distiller(palette = "Blues", direction = -1) +
    theme_minimal() + 
    theme(axis.text.x = element_text(hjust = 1, angle = 45)) +
    labs(title = "Most Wins During Regular Season",
         x = "\nTeam",
         y = "Frequency\n",
         fill = "Frequency\n")

## Selecting by n

Metrics

reg_season_metrics <- df_regular_season_all %>%
  select(-c(season, n_day, team_name, index, outcome)) %>%
  colnames()

for (metric in reg_season_metrics) {
  figure <- df_regular_season_all %>%
    ggplot(., aes(x = eval(as.name((metric))), fill = outcome)) + 
      geom_density(alpha = 0.5, color = 0) +
      theme_minimal() + 
      labs(x = paste0("\n",metric),
           y = "Density\n")
  print(figure)
}

## Warning: Removed 36 rows containing non-finite values (stat_density).

It looks like the losing team shoots slightly more, but misses even more.

It looks like the winning team takes less shots, misses less, has more offensive rebounds, more assists, less turnovers, more steals, more blocks, and less personal fouls.

By Season

Most Wins

df_most_wins <- df_regular_season_all %>%
  group_by(season, team_name, outcome) %>%
  count() %>%
  ungroup() %>%
  filter(outcome == "win") %>%
  group_by(season) %>%
  top_n(10) %>%
  ungroup() %>%
  arrange(season, desc(n)) %>%
  mutate(order = row_number())

## Selecting by n

ggplot(df_most_wins, aes(x = order, y = n, fill = n)) + 
    geom_col() +
    scale_fill_distiller(palette = "Blues", direction = -1) +
    theme_minimal() + 
    theme(axis.text.x = element_text(hjust = 1, angle = 45)) +
    labs(title = "Most Wins During Regular Season",
         x = "\nTeam",
         y = "Frequency\n",
         fill = "Frequency\n") +
  facet_wrap(~ season, scales = "free") +
  scale_x_continuous(breaks = df_most_wins$order,
                     labels = df_most_wins$team_name,
                     expand = c(0,0))

Metrics

for (metric in reg_season_metrics) {
  figure <- df_regular_season_all %>%
    ggplot(., aes(x = eval(as.name((metric))), fill = outcome)) + 
      geom_density(alpha = 0.5, color = 0) +
      theme_minimal() + 
      labs(x = paste0("\n",metric),
           y = "Density\n") + 
      facet_wrap(~ season)
  print(figure)
}

## Warning: Removed 36 rows containing non-finite values (stat_density).